2013
Perez-Cruz, Fernando; Vaerenbergh, Steven Van; Murillo-Fuentes, Juan Jose; Lazaro-Gredilla, Miguel; Santamaria, Ignacio
Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances Artículo de revista
En: IEEE Signal Processing Magazine, vol. 30, no 4, pp. 40–50, 2013, ISSN: 1053-5888.
Resumen | Enlaces | BibTeX | Etiquetas: adaptive algorithm, Adaptive algorithms, classification scenario, Gaussian processes, Learning systems, Machine learning, Noise measurement, nonGaussian noise model, Nonlinear estimation, nonlinear estimation problem, nonlinear signal processing, optimal Wiener filtering, recursive algorithm, Signal processing, Wiener filters, wireless digital communication
@article{Perez-Cruz2013,
title = {Gaussian Processes for Nonlinear Signal Processing: An Overview of Recent Advances},
author = {Fernando Perez-Cruz and Steven Van Vaerenbergh and Juan Jose Murillo-Fuentes and Miguel Lazaro-Gredilla and Ignacio Santamaria},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6530761},
issn = {1053-5888},
year = {2013},
date = {2013-01-01},
journal = {IEEE Signal Processing Magazine},
volume = {30},
number = {4},
pages = {40--50},
abstract = {Gaussian processes (GPs) are versatile tools that have been successfully employed to solve nonlinear estimation problems in machine learning but are rarely used in signal processing. In this tutorial, we present GPs for regression as a natural nonlinear extension to optimal Wiener filtering. After establishing their basic formulation, we discuss several important aspects and extensions, including recursive and adaptive algorithms for dealing with nonstationarity, low-complexity solutions, non-Gaussian noise models, and classification scenarios. Furthermore, we provide a selection of relevant applications to wireless digital communications.},
keywords = {adaptive algorithm, Adaptive algorithms, classification scenario, Gaussian processes, Learning systems, Machine learning, Noise measurement, nonGaussian noise model, Nonlinear estimation, nonlinear estimation problem, nonlinear signal processing, optimal Wiener filtering, recursive algorithm, Signal processing, Wiener filters, wireless digital communication},
pubstate = {published},
tppubtype = {article}
}
2011
Ruiz, Francisco J R; Perez-Cruz, Fernando
Zero-Error Codes for the Noisy-Typewriter Channel Proceedings Article
En: 2011 IEEE Information Theory Workshop, pp. 495–497, IEEE, Paraty, 2011, ISBN: 978-1-4577-0437-6.
Resumen | Enlaces | BibTeX | Etiquetas: channel capacity, Channel Coding, Equations, Linear code, Noise measurement, noisy-typewriter channel, nontrivial codes, nonzero zero-error rate, odd-letter noisy-typewriter channels, Upper bound, Vectors, zero-error capacity, zero-error codes
@inproceedings{Ruiz2011,
title = {Zero-Error Codes for the Noisy-Typewriter Channel},
author = {Francisco J R Ruiz and Fernando Perez-Cruz},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6089510},
isbn = {978-1-4577-0437-6},
year = {2011},
date = {2011-01-01},
booktitle = {2011 IEEE Information Theory Workshop},
pages = {495--497},
publisher = {IEEE},
address = {Paraty},
abstract = {In this paper, we propose nontrivial codes that achieve a non-zero zero-error rate for several odd-letter noisy-typewriter channels. Some of these codes (specifically, those which are defined for a number of letters of the channel of the form 2n + 1) achieve the best-known lower bound on the zero-error capacity. We build the codes using linear codes over rings, as we do not require the multiplicative inverse to build the codes.},
keywords = {channel capacity, Channel Coding, Equations, Linear code, Noise measurement, noisy-typewriter channel, nontrivial codes, nonzero zero-error rate, odd-letter noisy-typewriter channels, Upper bound, Vectors, zero-error capacity, zero-error codes},
pubstate = {published},
tppubtype = {inproceedings}
}
2010
Vinuelas-Peris, Pablo; Artés-Rodríguez, Antonio
Bayesian Joint Recovery of Correlated Signals in Distributed Compressed Sensing Proceedings Article
En: 2010 2nd International Workshop on Cognitive Information Processing, pp. 382–387, IEEE, Elba, 2010, ISBN: 978-1-4244-6459-3.
Resumen | Enlaces | BibTeX | Etiquetas: Bayes methods, Bayesian joint recovery, Bayesian methods, correlated signal, Correlation, correlation methods, Covariance matrix, Dictionaries, distributed compressed sensing, matrix decomposition, Noise measurement, sensors, sparse component correlation coefficient
@inproceedings{Vinuelas-Peris2010,
title = {Bayesian Joint Recovery of Correlated Signals in Distributed Compressed Sensing},
author = {Pablo Vinuelas-Peris and Antonio Art\'{e}s-Rodr\'{i}guez},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5604103},
isbn = {978-1-4244-6459-3},
year = {2010},
date = {2010-01-01},
booktitle = {2010 2nd International Workshop on Cognitive Information Processing},
pages = {382--387},
publisher = {IEEE},
address = {Elba},
abstract = {In this paper we address the problem of Distributed Compressed Sensing (DCS) of correlated signals. We model the correlation using the sparse components correlation coefficient of signals, a general and simple measure. We develop an sparse Bayesian learning method for this setting, that can be applied to both random and optimized projection matrices. As a result, we obtain a reduction of the number of measurements needed for a given recovery error that is dependent on the correlation coefficient, as shown by computer simulations in different scenarios.},
keywords = {Bayes methods, Bayesian joint recovery, Bayesian methods, correlated signal, Correlation, correlation methods, Covariance matrix, Dictionaries, distributed compressed sensing, matrix decomposition, Noise measurement, sensors, sparse component correlation coefficient},
pubstate = {published},
tppubtype = {inproceedings}
}